Electroencephalographic spectral analysis to help detect depressive disorder

Ratna A. Apsari, Sastra K. Wijaya

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The increasing prevalence of depressive disorder (also known as major depressive disorder or MDD), especially in the younger generations, has brought urgency upon the importance of good mental health. Moreover, depression has proven to increase the risk of cardiovascular diseases, along with the severity of those diseases. Depressive disorders are oftentimes not diagnosed or misdiagnosed, because some of the symptoms are similar to those of other illnesses. Therefore, an electroencephalography-based system that could help diagnose this illness using a more quantitative approach is necessary to be developed. The goal of this study is to make a machine learning-based classification program using EEG signals to aid for the diagnostics of depression. EEG data of 19 channels were obtained from two data sources, Hospital Universiti Sains Malaysia and Leipzig Study of Mind, Body, and Emotion. The EEG data consisted of 31 depressed subjects and 30 healthy controls during resting conditions. These signals were processed using two different methods, which were wavelet transformation and Power Spectral Density (PSD). Relative power ratio and average alpha asymmetry were calculated for feature extraction. The classifier used was a feedforward neural network with cross validation. The highest achieved results were 83,6% accuracy using the wavelet method and 77,5% accuracy using the PSD method.

Original languageEnglish
Title of host publicationIBIOMED 2020 - Proceedings of the 37th International Conference on Biomedical Engineering
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages13-18
Number of pages6
ISBN (Electronic)9781728171562
DOIs
Publication statusPublished - 6 Oct 2020
Event37th International Conference on Biomedical Engineering, IBIOMED 2020 - Yogyakarta, Indonesia
Duration: 6 Oct 20208 Oct 2020

Publication series

NameIBIOMED 2020 - Proceedings of the 37th International Conference on Biomedical Engineering

Conference

Conference37th International Conference on Biomedical Engineering, IBIOMED 2020
Country/TerritoryIndonesia
CityYogyakarta
Period6/10/208/10/20

Keywords

  • Average alpha asymmetry
  • Depressive disorder
  • EEG
  • Feedforward neural network
  • Relative power ratio

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